Conference Agenda

With the swift entry of artificial intelligence (AI) into everyday life, human-product interactions are becoming increasingly complex. We suggest an ecosystem-minded, humanity-centered design approach to better understand this complexity. Simultaneously with the development of interaction types, discussions and developments on theories of mental models are crucial to understanding and improving the nature of these interactions. In this paper, we address the gap in mental model theories and extend Norman's conceptual model at three dialogue levels: dialogue in language, mind, and use.

With the shift from mechanical value delivery to mechatronic value delivery, development environments are becoming more complex. Intuitive decision-making in development management is becoming increasingly challenging. Meanwhile, the use project management software is spreading, bringing about a new level of project data for development projects, holding to potential to enhance human decision making. To this end, the paper presents an extension to factor analysis of mixed data, which can facilitate usage of exploratory data analysis to improve decision-making in development project planning.

This paper explores the role of integrating behavioral science to refine human-AI interaction, essential for ensuring safety and efficiency. Advocating for empathetic, user-centric design, the paper illustrates how behavioral insights can effectively inform AI-integrated designs, making AI applications more intuitive and ethically aligned with diverse human needs. This approach can ultimately enrich interaction across systems, fostering a more harmonious human-AI synergy.

This study proposes a diary method enabled with IoT sensors for user research in design. It addresses the limitations of diary methods by incorporating sensor data to trigger user self-reports. The focus is on how sensor data influences self-reports and designers' perceptions. Results show that sensor-enabled diaries offer more diverse content and overview of users’ lives and designers perceived the proposed method potentials, suggesting significant potential for IoT in user research.

Human-AI collaboration (HAIC) is a promising strategy to transform engineering design and innovation, yet how to design artificial intelligence (AI) to boost HAIC remains unclear. Accordingly, this paper provides a new, unified, and comprehensive scheme for classifying AI roles. On this basis, we develop an AI design framework that outlines expected AI capabilities, interactive attributes, and trust enablers across various HAIC scenarios, offering guidance for integrating AI into human teams effectively. We also discuss current advancements, challenges, and prospects for future research.

The role of Artificial Intelligence (AI) in design is clearly growing. One of the tenets of the paper is that stimulation could be among the design processes mostly benefitting from the introduction of AI. Available contributions have been reviewed to understand the current support AI can give in design inspiration and ideation. We also reflected on what AI should and ahould not do in the future: a framework is proposed. Based on the reviewed contributions, in no case, AI is seen as a substitute of designers. Most contributions originate from the IT domain and have a demonstrative purpose.